An empirical algorithm to seamlessly retrieve the concentration of suspended particulate matter from water color across ocean to turbid river mouths

Abstract(#br)We propose a globally applicable algorithm (GAA SPM ) to seamlessly retrieve the concentration of suspended particulate matter (SPM) ( C SPM ) from remote sensing reflectance ( R rs ( λ )) across ocean to turbid river mouths without any hard-switching in its application. GAA SPM is based on a calibrated relationship between C SPM and a generalized index for SPM ( GI SPM ) from water color. The GI SPM is mainly composed of three R rs ( λ ) ratios (671, 745, and 862 nm over 551 nm, respectively), along with weighting factors assigned to each ratio. The weighting factors are introduced to ensure the progressive application of R rs ( λ ) in the longer wavelengths for increasing C SPM . Calibration of GAA SPM employed data collected from multiple estuarine and coastal regions of Europe, China, Argentina, and the USA with the measured C SPM spanning from 0.2 to 2068.8 mg/L. Inter-comparison with several recalibrated well-known C SPM retrieval algorithms demonstrates that GAA SPM has the best retrieval accuracy over the entire C SPM range with a relative mean absolute difference (rMAD) of 41.3% (N = 437). This averaged uncertainty in GAA SPM -derived C SPM is mostly attributed to the retrievals from less turbid waters where C SPM < 50 mg/L (rMAD = 50%, N = 214). GAA SPM was further applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) measurements over prominent coastal areas and produced reliable C SPM maps along with realistic spatial patterns. In contrast, applications of other C SPM algorithms resulted in less reliable C SPM maps with either unjustified numerical discontinuities in the C SPM spatial distribution or unsatisfactory retrieval accuracy. Therefore, we propose GAA SPM as a preferred algorithm to retrieve C SPM over regions with a wide range of C SPM , such as river plume areas

Challenges and Solutions of Ocean Color Remote Sensing: In Situ Radiometric Measurements and Shallow Water Inversion

This study is dedicated to address several issues and the challenges in ocean color remote sensing. The objects of this study include: (1) investigation of the ship perturbation for in situ optical measurements; (2) investigation of the shading error of SBA system and the correction of its shading error; and (3) an innovative progressively image process scheme for retrieving bathymetry from high spatial resolution ocean color satellite products. The water color is an indicator of water properties and it could be observed remotely even from the space. From the satellites in the space, the water color information at large spatial scale could be acquired swiftly. Because of the dynamic of water, the investigation of global water requires large spatial coverage and short revisit period. Ocean color remote sensing is the only method that could satisfy such requirements. This study covers the issues of ocean color remote sensing specifically at in situ radiometric measurements and shallow water remote sensing. In situ radiometric measurements is an essential part in ocean color remote sensing and provides data for calibration and algorithm developments. Ship perturbation and self-shading error are the two issues that bring uncertainties to in situ measurements. Thus we exam the ship perturbation and the self-shading error (with skylight blocked approach (SBA) as demonstration) by Monte-Carlo simulations based on radiative transfer model. After that, from the simulations, a practical guidance is given about how to limit ship perturbation. Besides, built on simulated results, a scheme is developed to correct the self-shading error of SBA. Shallow water remote sensing requires data with high spatial resolution to capture spatial variances. Besides that, because of the complex optical environment, to achieve a reliable retrieval of water and bottom properties from a single pixel, sufficient (i.e., more than 7 bands) spectral information is required. However, none of the ocean color satellite satisfies these two simultaneously. By incorporating spatial correlation at the image, an progressively image processing (PIP) scheme is proposed. For high spatial resolution data, a significant improvement in bathymetry retrieval is found compared with the traditional method that ignoring spatial correlation

A low-cost digital colorimetry setup to investigate the relationship between water color and its chemical composition

Developments in digital image acquisition technologies and citizen science lead to more water color observations and broader public participation in environmental monitoring. However, the implications of the use of these simple water color indices for water quality assessment have not yet been fully evaluated. In this paper, we build a low-cost digital camera colorimetry setup to investigate quantitative relationships between water color indices and concentrations of optically active constituents (OACs). As proxies for colored dissolved organic matter (CDOM) and phytoplankton, humic acid and algae pigments were used to investigate the relationship between water chromaticity and concentration. We found that the concentration fits an ascending relationship with xy chromaticity values and a descending relationship with hue angle. Our investigations permitted us to increase the information content of simple water color observations, by relating them to chemical constituent concentrations in observed waters